1. Field of the Invention
The invention relates to a landmark detection apparatus and method for estimating a current position of an intelligent system during autonomous traveling.
2. Description of the Related Art
In recent years, interest in intelligent systems such as intelligent vehicle systems (for example, unmanned planes, unmanned vehicles, and mobile robots), unmanned surveillance systems, and intelligent transportation systems has grown. Accordingly, studies on such intelligent systems are being actively conducted. In particular, diverse localization methods are being suggested to help intelligent systems recognize their positions. In addition, localization methods using artificial or natural landmarks and based on optical technology are being presented.
Localization using a natural landmark is a more common method than localization using an artificial landmark and is suitable for both indoor and outdoor use. A natural landmark is selected within a scene with consideration of its geometrical or optical characteristics, and an intelligent system recognizes the natural landmark by its characteristics or feature amounts. However, localization using a natural landmark has a disadvantage in that it is difficult to extract an optimal landmark in an actual environment.
On the other hand, localization using an artificial landmark is a very simple and effective method for an intelligent system to estimate its position indoors. An intelligent system performs modeling of a landmark model, or feature amounts in a landmark model, in advance and, while traveling, locates an artificial landmark in an image. Therefore, in localization using an artificial landmark, it is important to select proper forms or feature amounts of a landmark and a proper landmark detection method.
Many landmark models have been suggested together with detection algorithms. Examples include a landmark model having certain forms, such as a bar code, or a black and white self-similar pattern. Since a localization method using information regarding certain forms of a landmark is heavily dependent on results of image processing, a landmark localization success rate is low due to a blurring phenomenon caused by noise or inaccurate focus in an image.
Meanwhile, a CONDENSATION (CONditional DENsity propagATION) algorithm is used for landmark detection. The CONDENSATION algorithm is known to be able to track an object in real time in a complicated environment. In the CONDENSATION algorithm, an entire region of each frame image is set as a sampling region, and an initial set value of a degree of dispersion is applied to each frame image. Therefore, in the CONDENSATION algorithm, landmark detection requires a lot of time, and, if the initial value of the degree of dispersion is set to a high value, detection accuracy is undermined. The CONDENSATION algorithm is disclosed in the paper “CONDENSATION-Conditional Density Propagation for Visual Tracking,” Int. J. Computer Vision, 1998, by M. Isard and A. Blake.